Process / pipeline
Genetic Algorithm — Evolutionary Optimization
A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.
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Sources
- Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗
- Deb, K. (2001). Multi-Objective Optimization using Evolutionary Algorithms. Wiley. ISBN: 9780471873396
Related methods
Referenced by
Agent-based ant colony optimizationAgent-based genetic algorithmAnt Colony OptimizationArithmetic Optimization AlgorithmArtificial Bee ColonyBayesian Genetic AlgorithmBayesian Simulated AnnealingCuckoo SearchDeterministic Genetic AlgorithmDeterministic Particle Swarm OptimizationDifferential EvolutionEvolutionary StrategyFirefly AlgorithmGrey Wolf OptimizerHarmony SearchHybrid Response Surface MethodologyHyper-HeuristicsMemetic AlgorithmMixed-Integer ProgrammingMulti-objective genetic algorithmMulti-Objective OptimizationNEATNSGA-IIParticle Swarm OptimizationPolicy Scenario Genetic AlgorithmRobust Genetic AlgorithmSimulated AnnealingSlime Mould AlgorithmStochastic Genetic AlgorithmStochastic Tabu SearchTabu SearchVariable Neighborhood SearchWhale Optimization Algorithm